Podcast interview with Matt Wright, Stitched.io

@rvanbruggen If you need another podcast speaker….let me know…I’ll come along and talk Social Networking Theory and neo4j if you like?
— MrMattWright (@MrMattWright) March 16, 2015

I did not have to think twice. Matt and I had a great conversation about stitched.io, and how they use Neo4j in anger. Super cool:

Here's the transcript of our conversation:

RVB: Hello, everyone. My name is Rik, Rik Van Bruggen from Neo Technology, and here we are again recording another podcast session for our Graph Database podcast. I'm joined today by Matt Wright from Stitched.io. Matt, welcome.

MW: Hi.

RVB: Hi. Matt, like everyone on the podcast, I just want to ask you who you are and what's your relationship to Neo Technology. Tell us a little bit about yourself.

MW: Okay. So, I'm Matt Wright. I'm the CTO at Stitched.io. And we're essentially-- we've got a sort of grand mission, I guess, which is to try and help build better teams for every company in the world. I guess that's our little elevator pitch. And at the moment we are sort of focused recruitment and we've built a product and we use Neo4j to build a CRM system basically. So our CRM system is built entirely on Neo4j, and then we use that for various things.

RVB: Wow, that's great. How long have you been working on that?

MW: I guess last year we were a science project and now we're an actual start-up [chuckles]. We've just sort of done a bunch of seed funding, so we're kind of going to market in the next couple of months and kind of taking it from there.

RVB: Wow, that's cool.

MW: It's been going since probably about the middle of last year in seriousness.

RVB: Cool. Well, everyone on the podcast gets a couple of questions here. And the first one that I really want to ask you is what attracted you to Graph Databases? What do you love about Graph Databases? Why is it the best thing since sliced bread?

MW: This is an easy one to answer for us. Initially we were attracted by-- my co-founder is a guy that owns a recruitment company and we started working on a problem that is kind of common for recruitment companies, which is they have this big body of contacts, very much like LinkedIn but sort of their private world, and they wanted to kind of model that and make use of it. So, we did a bunch of research and we found Neo4j, and it's like, "Hey, we can build a whole social network out of this thing." So, we started there and we've kind of done that. And then we moved on from there. It's like, "Well, how can we use this thing?" We built recommendation engines on Neo4j. All of our stuff is in Java. And so, we built recommendation engines saying, "Hey, who knows who?"- you know, the typical social networking stuff. We've also built an ontology in Neo4j to sort of say, "Hey, when you search for AngularJS, you should also search for Backbone and Ember and React, and these other things." We've tied that all together and built a big search engine.

RVB: Wow.

MW: Our search runs all of that stuff in just under a couple hundred milliseconds and there's absolutely no chance we could do that without Neo4j.

RVB: Wow. Did you look at anything else? Did you look at other types of databases before you started?

MW: Well, we started-- our first attempt was a triple store on PostgreSQL and it was horrifically slow. So, I think we probably started with Neo4j, and kind of evolved our way to our product. Like, we're very sort of graph driven, and our whole thing is about connectedness, and possible connectedness. It's a sort of-- I think, in terms of recommendation engines, you can't really find something that would do what we want, that will be this quick.

RVB: Fantastic, so how important is the real-time aspect of the recommendation? Is that a big part of the solution then?

MW: It's massive. A lot of our users are recruitment guys, and they're all rushed for time, and also they're on the phone constantly with people. So, if you can say, "Oh, you've done this skill. Oh, have you used this skill, and this skill, and this skill?" And you can use a recommendation engine to do that in real time. That's really, really valuable.

RVB: Oh, wow, fantastic. Cool. It sounds like a fantastic project. So, people can take a look at it at Stitched.io, right?

MW: Yep, you can go and look at our Superman guy and yeah, if anybody wants to-- actually we have a blog as well which goes through a lot of technical detail on how we do all the things. We have a sort of very heavy Angular front end so there's a good blog post on that. And there'll be a few more coming out and about the Java stack and some sort of social networking theory as well. That's like Stitched.io/blog and you'll get there.

RVB: Yeah, fantastic, and I think you guys did a talk at the London meet-up last year as well, didn't you?

MW: Yeah, that's right. I'm probably due to give you guys another one because we've definitely got some updated bling to show everybody [chuckles].Fantastic, okay, we'll do that. Wrapping up here with the same question that I ask everyone, where do you think this is going? Where is Stitched.io going? Where is graph databases going? What do you want from graph databases in the future? Could you give us your perspective?

RVB: Yeah, sure and I have a sort of a related question back actually, have you read that book Connected - the orange one?Absolutely, I have, yeah.

MW: It's like the best book ever.

RVB: It's fantastic.

MW: Outside of our stuff, the graph databases can be applied to a billion different things. There's some fantastic stuff in there about the spread of disease and the spread of obesity, the internet of things and controlling population growth and all the stuff is like--

RVB: Lots of great examples, right?

MW: There's a start-up every two pages.

RVB: It's fantastic, yeah [chuckles].

MW: And I think one of the other things is starting to measure sentiments and the way that emotion travels through and all of this stuff we just haven't touched the sides of. And there's some excellent tech stuff coming out from Kenny Bastani that's a very, very good bit of tech - Maze Runner - and we're gonna have a look at that which is sort of the off graph analytics.

RVB: That's the integration with Spark, right?

MW: Yeah, exactly. That's pretty exciting. But in general, I think there's a world of applications out there that graphs can be used for.

RVB: Fantastic. Cool. Matt, we're going to wrap up here. Thanks a lot for taking the time to talk to me and--